Optical mode optimization for wafer inspection

ABSTRACT

According to some embodiments, the present disclosure provides a method for determining wafer inspection parameters. The method includes identifying an area of interest in an IC design layout, performing an inspection simulation on the area of interest by generating a plurality of simulated optical images from the area of interest using a plurality of optical modes, and selecting, based on the simulated optical images, at least one of the optical modes to use for inspecting an area of a wafer that is fabricated based on the area of interest in the IC design layout.

PRIORITY

The present application is a continuation of U.S. patent applicationSer. No. 17/121,174 filed Dec. 14, 2020, which is a continuation of U.S.patent application Ser. No. 16/250,128 filed Jan. 17, 2019 which claimspriority to U.S. Provisional Patent Application Ser. No. 62/732,813filed Sep. 18, 2018, each of which is incorporated herein by referencein its entirety.

BACKGROUND

The semiconductor integrated circuit (IC) industry has experienced rapidgrowth. Technological advances in IC materials and design have producedgenerations of ICs where each generation has smaller and more complexcircuits than the previous generation. However, these advances haveincreased the complexity of processing and manufacturing ICs. In thecourse of integrated circuit evolution, functional density (i.e., thenumber of interconnected devices per chip area) has generally increasedwhile geometry size (i.e., the smallest component that can be createdusing a fabrication process) has decreased. This scaling down processgenerally provides benefits by increasing production efficiency andlowering associated costs.

As a part of the IC fabrication process, wafers may be inspected toidentify potential defects. Typically, inspection may be done using anoptical system but as there are various types of defects that exhibitdifferent optical properties, inspection systems need to carefully tuneoptical parameters for optimal detection. Existing inspection systemsrequire repetitive inspections of real defects on real wafers in orderto optimize the optical parameters. In other words, the inspection is atime-consuming process often with low efficiency. Consequently, althoughexisting inspection systems have been generally adequate for theirintended purposes, they have not been satisfactory in all respects.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present disclosure are best understood from the followingdetailed description when read with the accompanying figures. It isemphasized that, in accordance with the standard practice in theindustry, various features are not drawn to scale. In fact, thedimensions of the various features may be arbitrarily increased orreduced for clarity of discussion.

FIG. 1 is a schematic diagram illustrating a method for determiningwafer inspection parameters according to various aspects of the presentdisclosure.

FIG. 2A is a schematic diagram illustrating a method for retrainingwafer inspection parameters according to various aspects of the presentdisclosure.

FIG. 2B is a schematic diagram illustrating an example artificialintelligence (AI) structure for optimizing optical parameters accordingto various aspects of the present disclosure.

FIGS. 3A, 3B, 3C, 3D, 3E, 3F, 3G, 3H, and 3I illustrate simulation casesthat demonstrate the evaluation and selection of optical parameters fordifferent types of defects.

FIG. 4 is a block diagram of an inspection system capable ofimplementing the various disclosed methods according to various aspectsof the present disclosure.

FIG. 5 is a schematic diagram of a computer system capable ofimplementing the various disclosed methods according to various aspectsof the present disclosure.

DETAILED DESCRIPTION

The following disclosure provides many different embodiments, orexamples, for implementing different features of the invention. Specificexamples of components and arrangements are described below to simplifythe present disclosure. These are, of course, merely examples and arenot intended to be limiting. For example, the formation of a firstfeature over or on a second feature in the description that follows mayinclude embodiments in which the first and second features are formed indirect contact, and may also include embodiments in which additionalfeatures may be formed between the first and second features, such thatthe first and second features may not be in direct contact. In addition,the present disclosure may repeat reference numerals and/or letters inthe various examples. This repetition is for the sake of simplicity andclarity and does not in itself dictate a relationship between thevarious embodiments and/or configurations discussed. Moreover, variousfeatures may be arbitrarily drawn in different scales for the sake ofsimplicity and clarity.

Further, spatially relative terms, such as “beneath,” “below,” “lower,”“above,” “upper” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. The spatiallyrelative terms are intended to encompass different orientations of thedevice in use or operation in addition to the orientation depicted inthe figures. For example, if the device in the figures is turned over,elements described as being “below” or “beneath” other elements orfeatures would then be oriented “above” the other elements or features.Thus, the exemplary term “below” can encompass both an orientation ofabove and below. The apparatus may be otherwise oriented (rotated 90degrees or at other orientations) and the spatially relative descriptorsused herein may likewise be interpreted accordingly.

The present disclosure is generally related to semiconductor devices andinspection methods thereof, and more particularly to optical modeoptimization for wafer inspection.

As a part of the IC fabrication process, wafers undergo inspection toidentify potential defects. Inspection is an important part offabricating semiconductor devices such as ICs. Inspection systems oftenhave adjustable optical parameters such that different opticalparameters may be used to detect different defect types or to minimizeunwanted signals such as background noise. However, since there are manytypes of defects that exhibit different optical properties, inspectionsystems need to carefully tune optical parameters for optimal detection.Current methods for tuning an optical inspection recipe requirerepetitive inspections of real defects on real wafers in order todetermine effective optical parameters. However, such an inspectionprocess is time-consuming if all potential defects generated by theinspection process are to be reviewed. The issue is exacerbated by thefact that an inspection system has multiple categories of opticalparameters, each of which has multiple value options. That means theinspection system has many (e.g., close to 100) possible combinations ofoptical parameter values. Testing all these possibilities on real wafersis inefficient and time-consuming. Further, even if a preferred set ofoptical parameter has been identified for a particular defect, iffabrication process conditions change (as they often do), the set ofoptical parameters may no longer work since the defect may have changedin terms of shape and/or location.

The present disclosure solves these issues by optimizing inspectionparameters not via inspection of real wafers but via inspectionsimulation. The inspection simulation allows a user not only toinitially identify an optimal set of optical parameters for inspecting acertain defect, but also to adjust or tune the optical parameters shouldprocess conditions change. Each set of optical parameters is consideredan optical mode. According to some embodiments, to optimize an opticalmode for a particular defect of interest (DOI), an inspection systemfirst uses a 3D model of an IC design layout to identify an area thereinthat surrounds the location of the DOI. An inspection simulation is thenperformed on the area by generating multiple simulated optical imagesusing multiple candidate optical modes. The simulated optical images arethen compared against each other to determine which one represents thebest result, and the corresponding optical mode is then selected as thepreferred mode. Further, after the initial optimization, the inspectionsystem is capable of monitoring process conditions such that when newDOIs are discovered, the system may be retrained by adjusting values ofoptical parameters. As a result, the present disclosure allows foroptimization of optical parameters without having to test all possibleoptical modes on real wafers with real defects. The optimized opticalparameters may be used for subsequent inspections of similar DOIs andgenerally do not require much additional tuning, if any. In case tuningsare needed, the retrain mechanisms disclosed herein allow for fasttuning of optical parameters to adapt to process condition changes.

FIG. 1 is a schematic diagram illustrating a method 100 for determiningwafer inspection parameters according to various aspects of the presentdisclosure. Method 100 is merely an example, and is not intended tolimit the present disclosure beyond what is explicitly recited in theclaims. Additional operations can be provided before, during, and aftermethod 100, and some operations described can be replaced, eliminated,or moved around for additional embodiments of method 100.

Method 100 may be used to detect various defects, which may beclassified as systematic defects, systematic random defects, and randomor nuisance defects. On one hand, systematic defects and systematicrandom defects are sometimes collectively referred to as “hot spots.”Such hot spots may be caused by problems related to circuit design, maskdesign, or even by the implementation of optical proximity correction(OPC) features. These issues may manifest themselves in every waferand/or chip. In other words, tuning fabrication process parameters willnot reduce or eliminate systematic defects or systematic random defects.As such, it is important to identify the systematic defects orsystematic random defects so that corrective actions may be taken toreduce or eliminate these defects from future wafers. For example,modifications may be made to the photomask or the circuit design. On theother hand, random defects or nuisance defects may be attributed to avariety of factors such as unexpected/unforeseen fabrication processvariations, contaminant particles, human errors, machine errors, etc. Assuch, the random defects or nuisance defects cannot be completelyeliminated even if circuit or mask designs are optimized. Theiroccurrence may be unrepeatable and random in nature. For reasons ofsimplicity, systematic defects and systematic random defects may bereferred to herein as hot spots, and random defects and nuisance defectsmay be referred to herein as nuisance defects. A defect of interest(DOI) is any defect that a wafer inspector is interested in detecting.Both hot spots and nuisance defects may be DOIs during inspection. Insome embodiments, DOIs are hot spots whose locations and/or shapes aremore predictable than nuisance defects, for example, based on theexperience of users.

As part of the wafer inspection process, method 100 optimizes waferinspection parameters that are to be used for inspecting a potential DOI102. To start off, at block 110, a portion of an IC design layout 111 isselected as an area of interest (AOI), in short as area 112. In anembodiment, area 112 is selected because empirical data suggest that DOI102 is likely to occur within area 112. Thus, area 112 may have anysuitable size or geometric shape as long as it surrounds DOI 102 andincludes pertinent layout information. The size of area 112 is bigenough to reflect layout information surrounding DOI 102 but smallenough for the inspection system to efficiently process duringsimulation. In some embodiments, the size of area 112 is about 200×200square micrometers (um{circumflex over ( )}2) or less (e.g., about 50×50um{circumflex over ( )}2, or about 10×10 um{circumflex over ( )}2). TheIC design layout and area 112 therein may exist in any suitable form.For instance, the IC design layout or area 112 may be a 3D model orlayout structure built from a Graphic Data System (GDS) or GDS II file.

Next, two different versions of area 112—one with DOI 102 and the otherwithout DOI 102—are simulated to generate a simulated optical image.Specifically, at block 120, an artificial defect is added to area 112 torepresent DOI 102, which is possible to occur within area 112 duringwafer fabrication of the IC design layout. The artificial defect is nota real defect on a wafer but is added to area 112 by a user of theinspection system so as to determine the impact of DOI 102 on inspectionoutputs. DOI 102 may be of any suitable type such as a cut metalfailure, a damaged oxide definition fin, a dummy gate or metal gateextrusion, merged metal lines, missing vias, or another type ofpotential defects. Examples of such defects are further described withrespect to FIGS. 3A-3I. In an embodiment, when the artificial defect isadded into area 112, relevant information such as the type of DOI 102and the location of DOI 102 (e.g., 2D or 3D coordinates, either relativeor absolute) is specified by the inspection system. A user may know(e.g., based on experience) what type of defect is likely to occur atcertain locations of an IC design layout, so the user may input suchinformation into the inspection system.

At block 130, an inspection simulation is performed on area 112 with andwithout DOI 102. In an embodiment, the inspection simulation is firstperformed on area 112 with DOI 102 to generate a first image 132, andthe inspection simulation is then repeated on the clean version of area112 to generate a second image 134. Since images 132 and 134 do notdepend on each other, they may be generated in any order. Images 132 and134 may be generated from respective 3D models of area 112. Further, thesame optical mode 136 (called a current optical mode) is used on bothversions of area 112 in order to properly evaluate the impact of DOI102.

In practice, an inspection system is capable of operating in variousoptical settings that are called optical modes. Each optical modeincludes a set of optical parameters. In an embodiment, the set ofoptical parameters include pixel size, optical wavelength, apertureshape, optical polarization, and focus setting. These parametersrepresent five broad categories of tunable optical properties. Eachoptical parameter has options of different values (in numbers orranges). For example, the pixel size may be 10 nm, 20 nm, 50 nm, 100 nm,or any other suitable value. The optical wavelength may be divided intoranges including: ultra-deep band (UDB) which is part of the deepultraviolet band, middle band (MB), blue band (BL) which is part of theultraviolet band, IL (about 370 nm), GL (about 430 nm), GHI (IL to GL))band which is close to visible light, or any other suitable opticalwavelength. The aperture shape may be horizontal low sigma (HDIB), edgecontrast plus (ECP), vertical low sigma (VDIB), bright field (BF), orany other suitable optical mask aperture. The optical polarization maybe horizontal (polarizer 1) none (polarizer 2) (HN) or vertical(polarizer 1) none (polarizer 2) (VN). The focus setting may be −0.2, 0,0.2 (no unit in the software), or any other suitable value. In someembodiments, pixel size dominates an inspection resolution, spectrum(optical wavelength) has high correction with material and semiconductorstructure, aperture shape may separate a background noise and a defectsignal, polarization is considered with surface topography, and focussetting or decision is dependent on defect location(s) on a wafer. Thesefive optical parameters also influence each other, so they may beconsidered together to achieve an optimal set of optical parameters.Since there are five tunable optical parameters, and each opticalparameter has various values, the inspection system has many possiblecombinations of optical parameters values, where each combination may beconsidered an optical mode. When the inspection system is simulated in asimulation system (either part of the inspection system or a standalonesystem), the same optical modes may be adopted for analysis andoptimization. In this sense, the simulation system also operates invarious optical modes.

The inspection simulation continues at block 140, where a differenceimage 142 is generated by comparing first and second simulation images132 and 134. In an embodiment, image 132 is subtracted from image 134,or vice versa, to generate difference image 142. In this case,difference image 142 is the simulated optical image. Note that, sincethe simulated optical image represents an optical image of area 112generated by the inspection simulation process, any suitablerepresentation of area 112 with DOI 102 may be used as the simulatedoptical image as long as its quality can be properly evaluated. Forinstance, instead of using difference image 142, in some embodimentsfirst image 132 may be directly used as the simulated optical imagesince it is generated from area 112 with DOI 102 (in which case block140 may be omitted).

A simulation system may use any suitable algorithms to perform aninspection simulation on area 112. In an embodiment, the simulationsystem is based on finite-difference time-domain (FDTD) equations tosolve Maxwell boundary conditions of area 112. The simulation system mayconsider reflective light, as opposed to optical proximity correction(OPC)-related FDTD applications which may consider only transmit light.The simulation system may also use the convolutional perfectly matchedlayer (CPML) absorption boundary condition as part of the simulationalgorithm. The simulation system may also utilize near-structuredetectors with incident light filtering technology.

At block 150, one or more attributes of difference image 142 arecomputed or otherwise determined as measures to evaluate the performanceof optical mode 136. The attributes may help determine whetherdifference image 142 allows an inspection system to identify theexistence (or nonexistence) of DOI 102. Various predefined attributesmay be used for evaluating optical mode 136. For example, the predefinedattributes include the number of defects that can be detected as well asthe strength of detection signals. In some embodiments, the inspectionsystem uses a multi-die adaptive threshold (MDAT) algorithm, which maycalculate various attributes that describe the relations between defectand background signals. In this case, the predefined attributes mayinclude an MDAT signal, an MDAT noise, an MDAT offset signal thataccounts for a weighted difference between the MDAT signal and the MDATnoise, or a signal to noise (S/N) ratio that accounts for a ratiobetween the MDAT signal and the MDAT noise, or combinations thereof. Insome embodiments, the attributes may depend on the type of DOI 102 beingtargeted. For instance, if DOI 102 is missing via(s), the attributes mayinclude the number of detected missing vias.

Since the attributes of difference image 142 are detectable by theinspection system, such attributes may be considered defect detectionsignals used for the evaluation of optical mode 136. In this case, oneor more defect detection signals are generated from the simulatedoptical image (e.g., difference image 142) for evaluating optical mode136 against other optical modes. A defect detection signal may bedirectly generated from difference image 142 (e.g., an MDAT signal or anMDAT noise). A defect detection signal may also be indirectly generatedfrom difference image 142 (e.g., an MDAT offset or a signal to noiseratio). In some embodiments, when two optical modes are being evaluated,one or more first defect detection signals are generated from a firstsimulated optical image, which had been generated using a first opticalmode. Similarly, one or more second defect detection signals aregenerated from a second simulated optical image, which had beengenerated using a second optical mode. Then, the first and secondoptical modes are evaluated by comparing the first and second defectdetection signals. Details of the evaluation process are furtherdescribed with respect to FIG. 3A-3I.

At block 160, method 100 determines whether optical mode 136 generatesthe best or otherwise satisfactory attributes. If yes, method 100 mayproceed to block 170, where optical mode 136 is selected as a preferredmode for future inspection of DOI 102 in area 112. Optical mode 136 issaved in a database (called a defects bank or library). Otherwise, ifoptical mode 136 generates unsatisfactory results (e.g., not better thanpreviously tested optical modes), method 100 may proceed to block 180,where one or more optical parameter values of optical mode 136 arechanged. For instance, the pixel size, optical wavelength, apertureshape, optical polarization, and focus setting, or combinations thereofmay be changed. At this point, optical mode 136 changes into a newoptical mode (which may also be considered a revised optical mode 136).

After changing one or more values of the optical parameters at block180, method 100 returns to block 130, where the inspection simulation isrepeated on area 112, with and without DOI 102, using the new opticalmode (or the revised optical mode 136). Similar to descriptions above, anew difference image is generated, and then attributes of the newdifference image are computed for evaluating the new optical modeagainst the original optical mode 136. In some embodiments,corresponding attributes of the two difference images are comparedagainst each other when two optical modes are being evaluated. Aftercomparison, the optical mode that generates better attributes (based onpredefined criteria) is selected as the preferred optical mode forfuture inspection of DOI 102 in area 112. This optical mode is thenadded to the defects bank, which stores information relevant to defectssuch as defect shapes, locations, and surroundings. In an embodiment,certain blocks of method 100 (e.g., blocks 130, 140, 150, 160, and 180)may be repeatedly executed until a preferred optical mode is identifiedor until optical mode 136 generates satisfactory results. The preferredoptical mode is sometimes called a best known method (BKM) mode.

In some embodiments, some blocks of method 100 are manually performed bya user of the optical inspection system. For example, method 100 maygenerate a set of attributes from each optical mode, and then offersmultiple sets of attributes for the user to decide which optical modeworks best. The inspection system may select a few promising opticalmodes and present them as candidates for final selection by the user.

Various suitable techniques may be used to optimize the optical mode. Insome embodiments, values of the same optical image attributes, whichhave been computed from images of different optical modes, are comparedagainst each other to determine which optical mode generates relativelybetter attributes. In an embodiment, attribute values such as S/N ratiosare directly compared, and the optical mode resulting in the highest S/Nratio is selected as the best optical mode. Alternatively, eachattribute value may be ranked according to a ranking system, and thehighest ranked attribute value may correspond to the best optical mode.In some embodiments, multiple attributes of simulated optical images areevaluated in a comprehensive manner. For example, a first simulatedoptical image may be better than a second simulated optical image interms of a first attribute, but may be worse than the second simulatedoptical image in terms of a second attribute. In this case, first andsecond attributes may be evaluated together (e.g., with more weightgiven to the more important attribute) to determine which simulatedoptical image produces overall better attributes.

In some embodiments, artificial intelligence (AI) algorithms, such asmachine learning, deep learning, random forest, and/or mixed modelalgorithms are used to select the optimal optical mode. Such AIalgorithms help the inspection system to identify BKM modes moreefficiently, for example, by learning from past comparisons and byoffering suggestions or recommendations to a user of the opticalinspection system. More details of the AI algorithms are described belowwith reference to FIG. 2B.

Once a preferred optical mode has been identified using method 100, thepreferred optical mode may be used to inspect a certain DOI for a longtime, provided that fabrication process conditions do not changesignificantly. However, sometimes fabrication process conditions dochange, which may impact the formation of DOIs on wafers. For example,the location and/or type of DOIs may change. As a result, a preferredoptical mode may lose its effectiveness over time. Therefore, a retrainmechanism is disclosed herein to help maintain the effectiveness of theinspection system.

FIG. 2A is a schematic diagram illustrating a method 200 for retrainingwafer inspection parameters according to various aspects of the presentdisclosure. Like method 100, method 200 is merely an example, and is notintended to limit the present disclosure beyond what is explicitlyrecited in the claims. Additional operations can be provided before,during, and after method 200, and some operations described can bereplaced, eliminated, or moved around for additional embodiments ofmethod 200.

Method 200 may be used in conjunction with method 100 to detect variousdefects. To start off method 200, at block 210, an inspection systemencounters a potentially new DOI. The new DOI may be a new type of DOIat a known location, a known type of DOI at a new location, or a newtype of DOI at a new location. At block 220, the inspection system mayconduct a layout similarity check to identify a known DOI that isclosest to the new DOI (in terms of type, location, or both). Asdescribed above, the known DOI has a preferred optical mode associatedtherewith. The layout similarity check may use any suitable algorithmsto compare DOIs. After identifying the known DOI, at block 230 theinspection system may use the preferred optical mode for the known DOIto inspect the new DOI. Any suitable inspection mechanisms may be used.At block 240, one or more attributes or defect detection signals arecomputed or otherwise determined to evaluate the preferred optical mode(specifically, the set of optical parameters contained therein).Depending on the DOI, any suitable attributes may be used. At block 250,method 200 determines, based on the attributes, whether the preferredoptical mode passes or fails predefined criteria. For example, eachattribute value may be ranked according to a ranking system, and anyrank that is lower than a threshold rank is deemed to have failed thecriteria. For another example, each attribute value may be accorded ascore, and a passing score may be assigned. Multiple attributes may beconsidered in a combined manner as well. If, at block 250, the preferredoptical mode passes predefined criteria, method 200 proceeds to block260, where the inspection system adopts the preferred optical mode forfuture inspections of the new DOI. The new DOI may be added to thedefect bank. Otherwise, if the preferred optical mode fails predefinedcriteria, method 100 proceeds to block 270, where the initial process ofidentifying a preferred optical mode would be repeated, as describedwith respect to method 100. In that process, the optical mode ischanged, for example, by modifying the values of one or more opticalparameters. After block 260, a new optical mode is selected forinspection of the new DOI, and relevant information is added to thedefect bank.

As described above, a defect bank initially stores information obtainedfrom method 100, including optical modes, defect types, locations, andsurrounding layout information. The initial defect may further includeempirical data obtained from prior inspections. As more defects areidentified in continuous use of the inspection system, informationregarding new defects may be incorporated into the defect bank. If thedefect bank has an optimal optical mode for one defect, but several newdefects have been found where this mode no longer works well (e.g., dueto change in process conditions), the inspection system has machinelearning capabilities to establish new modes.

As described above, in some embodiments, artificial intelligence (AI) isused to help select the optimal optical mode. FIG. 2B is a schematicdiagram illustrating an example AI structure 280 according to variousaspects of the present disclosure. AI structure may be implemented aspart of method 100 and/or method 200. As shown in FIG. 2B, AI structure280 may comprise attribute computation module 282, training set 284,neural network 286, classifier layer 288, and random forest module 290.In some embodiments, optical image 292 is fed into AI structure 280 andreceived by attribute computation module 282 and training set 284 inparallel. On the one hand, attribute computation module 282 maycalculate attributes of optical image 292 such as defect signal, noisesignal, and defect topology information, etc. For instance, attributesincluding MDAT gray level (GL), MDAT offset, and defect size may bedetermined according to method 100 described above. On the other hand,training set 284 may provide suitable images to optical image 292 forcomparative analysis. Training set 284 may provide patch images fromreal defects, which contain different defect types that occurred beforein different process layers. The image may be processed by neuralnetwork 286 (e.g., a resnet 18 network) to generate attributes. In someembodiments, neural network 286 includes at least one convolution layerand/or a depth-wise separable convolution layer for computingattributes. Sometimes there may be a large set of attributes, in whichcase classifier layer 288 may determine and select best attributes foradditional analysis. In an embodiment, classifier layer 288 may includeat least one fully connected (FC) layer for attributes selection.Classifier layer 288 may be implemented as multiple stages, each ofwhich may reduce the number of attributes by half (e.g., from 256 to128, from 128 to 64, etc). Classifier layer 288 may determine and outputa defect probability (embedded with other attributes), which may beconnected to random forest module 290 to output a final defectprobability. Therefore, random forest module 290 mixes or combines bothoutputs of computation module 282 and classifier layer 288 in generatingthe final defect probability. The final defect probability may then beused to determine the detectability of a defect (e.g., a probability ofone means detection, while a probability of zero means no detection).Thus, the final defect probability may be used in method 100 or 200 tohelp optimize or retrain the optical modes. The upper portion of AIstructure 280 containing attribute computation module 282 is sometimescalled a machine learning portion, while the lower portion of AIstructure 280 containing training set 284, neural network 286, andclassifier layer 288 may be called a deep learning portion. Opticalimage 292 may represent a simulated optical image (e.g., when AIstructure 280 is used for optical mode optimization) or an actualinspection image (e.g., when AI structure 280 is being trained based ona database which includes many real known defects on real wafers, inwhich case the output defect probability may determine effectiveness ofAI structure 280). Overall, AI structure 280 may help optimize opticalmodes using the final defect probability.

FIGS. 3A-3I illustrate several simulation cases that demonstrate theevaluation and selection of optical parameters for different types ofDOIs (e.g., using method 100 and/or 200). FIG. 3A uses an example DOI toillustrate basic concepts involved in the disclosed inspectionsimulation. The example DOI in FIG. 3A is a cut metal failure, where asection of a cut metal is missing. The DOI is located in an example area112 with a size of about 2×2 um{circumflex over ( )}2. A first versionof area 112 has DOI 102 (which may be added to a 3D model of area 112),while a second version of area 112 does not have DOI 102. The inspectionsimulation is performed on the first version of area 112 to generatesimulated defect image 132, and the same inspection simulation isperformed on the second version of area 112 to generate simulated defectimage 134. The same optical mode is used on both versions of area 112during the inspection simulation. Image 132 is subtracted from image134, or vice versa, to generate difference image 142. Difference image142 is the simulated optical image on which various attributes may becomputed to evaluate the current optical mode. As described above,multiple optical modes may be tested and compared in the process ofidentifying a preferred optical mode.

FIG. 3B illustrates seven simulation tests where seven different opticalmodes are used to simulate the same area 112 as shown in FIG. 3A. Eachoptical mode has at least one optical parameter value that differs fromother optical modes. As noted in the diagram of FIG. 3B, optical mode 1uses a pixel size of 50 nm, a wavelength range of LB, an aperture shapeof BF, a polarization of VN, and a focus of 0. Optical mode 2 is thesame as optical mode 1, except that it uses a wavelength range of UDB.Optical mode 3 is the same as optical mode 1, except that it uses awavelength range of MB. Optical mode 4 is the same as optical mode 2,except that it uses an aperture shape of ECP. Optical mode 5 is the sameas optical mode 1, except that it uses a wavelength range of IL. Opticalmode 6 is the same as optical mode 2, except that it uses a polarizationof HN. Lastly, optical mode 7 is the same as optical mode 5, except thatit uses a polarization of HN. Further, the noise level is set to 10% inthe simulations unless otherwise noted.

Each optical mode generates a corresponding defect image (shown in FIG.3B) and a corresponding defect-free image (not shown in FIG. 3B), whichare then used to generate a corresponding difference image (shown inFIG. 3B). Predefined attributes—including an MDAT signal, an MDAT noise,an MDAT offset signal (which may or may not be a mathematical differencebetween the MDAT signal and the MDAT noise), and an MDAT S/N ratio—arecomputed for each difference image. For instance, optical mode 2 an MDATS/N ratio (=MDAT signal/MDAT noise) of about 9.7. Correspondingattributes of the seven optical modes are compared against each other.As illustrated in FIG. 3B, optical mode 2 generates the highest MDAT S/Nratio among the seven optical modes, and therefore is determined to bethe optimal optical mode. To verify the effectiveness of optical mode 2,an actual wafer with an actual DOI (as illustrated in FIG. 3A) has beeninspected using optical mode 2, and the actual inspection image is shownin FIG. 3B. The actual inspection image, which resembles the simulateddefect image of optical mode 2, has allowed for identification of theactual DOI. Therefore, the optical mode selection scheme disclosedherein proves to be an effective tool in identifying the DOI.

FIG. 3C illustrates an IC design layout area with a different type ofDOI, and FIG. 3D illustrates seven simulation tests where sevendifferent optical modes are used to simulate the area shown in FIG. 3C.The example DOI in FIG. 3C is a damaged oxide definition fin. Variousaspects of FIG. 3D are similar to that of FIG. 3B, and such similaraspects are not further elaborated in the interest of conciseness. Eachoptical mode has at least one optical parameter value that differs fromother optical modes. The optical parameter values of each optical modeare illustrated in the diagram of FIG. 3D. In some embodiments, multipleattributes of simulated optical images are evaluated in a comprehensivemanner. For example, as shown in FIG. 3D, optical mode 1 leads to thebest MDAT S/N ratio, but optical mode 2 leads to the lowest MDAT noiselevel, while optical mode 4 leads to the highest MDAT signal strength.Therefore, depending on the evaluation standard, each of these opticalmodes has its own benefits. Further, an optimal mode for fabrication(e.g., optical mode 2) may be different from an optimal mode forresearch & development (e.g., optical mode 4) due to differentevaluation standards (e.g., research & development may favor MDAT signalstrength, while fabrication may favor the MDAT S/N ratio). In someembodiments, instead of directly selecting a preferred optical mode, thesimulation system may offer several optical modes to a user forselection of the preferred mode. FIG. 3D shows that the top threeoptical modes in terms of MDAT S/N ratio are: optical modes 1, 2, and 4.Each of these top modes may be a viable option.

FIG. 3E illustrates an IC design layout area with another different typeof DOI, and FIG. 3F illustrates seven simulation tests where sevendifferent optical modes are used to simulate the area shown in FIG. 3E.The example DOI in FIG. 3E is one or more dummy gate or metal gateextrusions. Various aspects of FIG. 3F are similar to that of FIG. 3B,and such similar aspects are not further elaborated in the interest ofconciseness. Each optical mode has at least one optical parameter valuethat differs from other optical modes. The optical parameter values ofeach optical mode are illustrated in the diagram of FIG. 3F. In someembodiments, the simulation system may offer several optical modes to auser for selection of the preferred mode. FIG. 3F shows that the topthree optical modes in terms of MDAT S/N ratio are: optical modes 1, 2,and 6. Therefore, each of these three modes may be a viable option.Further, among these candidates, optical mode 2 leads to the lowest MDATnoise level, optical mode 6 leads to the highest MDAT signal strengthand the highest MDAT S/N ratio, and optical mode 1 leads to intermediatevalues for all of these attributes. Therefore, depending on theevaluation standard, each of these optical modes has its own benefits.In an embodiment, optical mode 1 is selected as a preferred mode forinspecting the DOI shown in FIG. 3E.

FIG. 3G illustrates an IC design layout area with another different typeof DOI, where two different optical modes are used to simulate the areashown in FIG. 3G. The example DOI in FIG. 3G is an unintended merging ofmetal lines. The two optical modes both use UDB as their opticalwavelength, but one simulation uses VN polarization while the other usesHN polarization. Two simulated defect images are generated from opticalmodes 1 and 2, respectively. As shown in FIG. 3G, attributes arecomputed from the two images, where the vertical axis represents an MDATgray level, and the horizontal axis represents an MDAT offset (biggervalue indicates better result). A comparison of the two diagrams in FIG.3G reveals that, since the VN polarization leads to higher attributevalues than the HN polarization, the VN polarization preferable over theHN polarization for inspecting the type of DOI shown in FIG. 3G.

FIG. 3H illustrates an IC design layout area with another type of DOI,where two different optical modes are used to simulate the same areashown in FIG. 3H. The example DOI in FIG. 3H is missing via(s). The twooptical modes use the same optical parameters except that one simulationuses an aperture shape of ECP while the other uses an aperture shape ofBF. Two simulated defect images are generated from the two opticalmodes, respectively. In this case, one helpful attribute is the numberof missing vias that can be detected and a noise signal level (denotedas SNV). A comparison of the two diagrams in FIG. 3H reveals that, sinceECP leads to a higher number of detected missing vias and a lower noisesignal level than BF, the aperture shape of ECP is preferable over theaperture shape of BF for inspecting the type of DOI shown in FIG. 3G.

FIG. 3I illustrates an IC design layout area with another type of DOI,where two different optical modes are used to simulate part of the areashown in FIG. 3I. The example DOI in FIG. 3I is a dummy gate or metalgate extrusion, which may occur at the middle end of line (MEOL). Asdescribed above, a predefined optical mode generates a simulated defectimage (shown in FIG. 3I) and a defect-free image (not shown in FIG. 3I),which are then used to generate a difference image (shown in FIG. 3I).Interestingly, the difference image reveals ripple-like patterns, whichare representative for the type of DOIs shown in FIG. 3I. Such uniquepatterns may help an inspection system identify the type of DOIs shownin FIG. 3I. To verify the effectiveness of the simulation, an actualinspection image of an actual wafer with this type of DOI has beentested. As shown in FIG. 3I, the ripple-like patterns indeed appear indifference images. Further, the ripple-like patterns are clearer incertain modes than other modes. Through the optical mode optimizationschemes disclosed herein, an optimal optical mode may be identified tomake the ripple-like patterns as clear as possible in order to maximizethe chances of detecting the type of DOI shown in FIG. 3I.

As disclosed herein, the embodiments of the present disclosure allow foroptimization of optical parameters without having to test all possibleoptical modes on real wafers with real defects. The optimized opticalparameters may be used for subsequent inspections of similar DOIs andgenerally do not require much additional tuning, if any. In case tuningsare needed, the retrain mechanisms disclosed herein allow for fasttuning of optical parameters to adapt to process condition changes. As aresult, the embodiments disclosed herein save time and increaseefficiency of wafer inspection systems.

FIG. 4 is a block diagram of an inspection system 400 suitable forimplementing various methods described herein, for example, method 100or 200. Inspection system 400 includes an optical system, which in turnincludes a light source 410, one or more optical path components such asa beam splitter 422 and a lens 424, and an optical signal detector 430.The optical system uses light to perform an optical inspection on awafer 440 in order to identify potential defects located thereon. In anembodiment, light from light source 410 may be directed to beam splitter422, which may be configured to direct the light from light source 410to wafer 440. Light source 410 may be implemented in any suitable mannerto generate optical energy of any suitable wavelength or wavelengthrange. The optical path components may include any other suitableelements (not shown) such as various types of lenses, apertures,filters, polarizing components, etc. The light may be directed to anyarea on wafer 440 at any suitable angle of incidence. Inspection system400 may scan the light over wafer 440 in any suitable manner. Lightreflected from wafer 440 may be collected and detected by detector 430.In an embodiment, light reflected from wafer 440 may pass through beamsplitter 422 to lens 424. Lens 424 may include a refractive opticalelement, and light collected by lens 424 may pass to detector 430.Detector 430 may include any suitable detector such as a charge coupleddevice (CCD) or another type of imaging detector.

Wafer 440 is fabricated based at least in part on an IC design layoutusing any suitable fabrication methods and processes. Wafer 440 includesvarious structures and layers fabricated on a substrate. Any suitablesubstrate may be used. The area of wafer 440 being inspected isfabricated based on the area of interest in an IC design layoutdisclosed herein. For example, the area of interest in the IC designlayout (e.g., a GDS file) is used during fabrication to create in thecorresponding area on wafer 440. The two areas may have the same orsimilar structures and layers (but one is virtual and the other isphysical). The area of wafer 440 may possibly contain a DOI thatoccurred during fabrication. Thus, to optimize the detection of such aDOI, an artificial defect representing the DOI may be added to the areaof interest in the IC design layout for the purpose of inspectionsimulation as described above. Note that wafer 440 needs not be createdfrom the specific file containing the IC design layout. Further, theinspected area of wafer 440 needs not have the same size or shape as thearea of interest in the IC design layout.

Inspection system 400 further includes (or is coupled to) a computersystem 1300, which is further described in FIG. 5 . Computer system 1300may include a simulation system disclosed herein, and computer system1300 communicates with detector 430 (or another part of the opticalsystem) to implement methods such as method 100 or 200. In anembodiment, the optical system optically inspects an area of wafer 440using an optical mode that is provided by computer system 1300. In turn,the optical system generates an optical image 432 for analysis bycompute system 1300. Although not shown in FIG. 4 , other types of data(e.g., IC design layout, DOI related information, defect detectionsignals, etc.) may be communicated between the optical system andcomputer system 1300. The optical image 432 may be any suitable type ofoutput signal, for example, images, image data, signals, image signals,which can be used by computer system 1300 or a user of inspection system400.

In an embodiment, as part of the process for retraining wafer inspectionparameters (e.g., using method 200), inspection system 400 performs anoptical inspection on an area of wafer 440 by using a set of opticalparameters to generate optical image 432 from wafer 440. One or moredefect detection signals may be generated from optical image 432 asdescribed herein, and inspection system 400 may determine, based on thedefect detection signals, whether to modify values of the set of opticalparameters for inspecting the area of wafer 440. The values of one ormore of the set of optical parameters may be modified if the defectdetection signals fails to meet predefined criteria, as described above.

FIG. 5 is a block diagram of computer system 1300 suitable forimplementing various methods and devices described herein, for example,the various method steps of the method 100 or 200. In variousembodiments, the computer system may include a tangible non-transitorycomputer readable medium comprising executable instructions. Theseexecutable instructions, when executed by one or more electronicprocessors, cause the one or more electronic processors to perform thesteps of the method 100 or 200. In some implementations, devices capableof performing the steps may comprise a network communications device(e.g., mobile phone, laptop, personal computer, tablet, etc.), a networkcomputing device (e.g., a network server, a computer processor, anelectronic communications interface, etc.), or another suitable device.Accordingly, it should be appreciated that the devices capable ofimplementing the method 100 or 200 may be implemented as the computersystem 1300 in a manner as follows.

In accordance with various embodiments of the present disclosure, thecomputer system 1300, such as a network server or a mobilecommunications device, includes a bus component 1302 or othercommunication mechanisms for communicating information, whichinterconnects subsystems and components, such as processor 1304 (e.g.,processing component, micro-controller, digital signal processor (DSP),etc.), system memory component 1306 (e.g., RAM), static storagecomponent 1308 (e.g., ROM), disk drive component 1310 (e.g., magnetic oroptical), network interface component 1312 (e.g., modem or Ethernetcard), display component 1314 (e.g., cathode ray tube (CRT) or liquidcrystal display (LCD)), input component 1316 (e.g., keyboard), cursorcontrol component 1318 (e.g., mouse or trackball), and image capturecomponent 1320 (e.g., analog or digital camera). In one implementation,disk drive component 1310 may comprise a database having one or moredisk drive components.

In accordance with embodiments of the present disclosure, computersystem 1300 performs specific operations by processor 1304 executing oneor more sequences of one or more instructions contained in system memorycomponent 1306. Such instructions may be read into system memorycomponent 1306 from another computer readable medium, such as staticstorage component 1308 or disk drive component 1310. In otherembodiments, hard-wired circuitry may be used in place of (or incombination with) software instructions to implement the presentdisclosure.

Logic may be encoded in a computer readable medium, which may refer toany medium that participates in providing instructions to processor 1304for execution. Such a medium may take many forms, including but notlimited to, non-volatile media and volatile media. In one embodiment,the computer readable medium is non-transitory. In variousimplementations, non-volatile media includes optical or magnetic disks,such as disk drive component 1310, and volatile media includes dynamicmemory, such as system memory component 1306. In one aspect, data andinformation related to execution instructions may be transmitted tocomputer system 1300 via a transmission media, such as in the form ofacoustic or light waves, including those generated during radio wave andinfrared data communications. In various implementations, transmissionmedia may include coaxial cables, copper wire, and fiber optics,including wires that comprise bus 1302.

Some common forms of computer readable media includes, for example,floppy disk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, carrier wave, or anyother medium from which a computer is adapted to read.

In various embodiments of the present disclosure, execution ofinstruction sequences to practice the present disclosure may beperformed by computer system 1300. In various other embodiments of thepresent disclosure, a plurality of computer systems 1300 coupled bycommunication link 1330 (e.g., a communications network, such as a LAN,WLAN, PTSN, and/or various other wired or wireless networks, includingtelecommunications, mobile, and cellular phone networks) may performinstruction sequences to practice the present disclosure in coordinationwith one another.

Computer system 1300 may transmit and receive messages, data,information and instructions, including one or more programs (i.e.,application code) through communication link 1330 and communicationinterface 1312. Received program code may be executed by processor 1304as received and/or stored in disk drive component 1310 or some othernon-volatile storage component for execution.

Where applicable, various embodiments provided by the present disclosuremay be implemented using hardware, software, or combinations of hardwareand software. Also, where applicable, the various hardware componentsand/or software components set forth herein may be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein may be separated into sub-components comprising software,hardware, or both without departing from the scope of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components may be implemented as hardware components andvice-versa.

Software, in accordance with the present disclosure, such as computerprogram code and/or data, may be stored on one or more computer readablemediums. It is also contemplated that software identified herein may beimplemented using one or more general purpose or specific purposecomputers and/or computer systems, networked and/or otherwise. Whereapplicable, the ordering of various steps described herein may bechanged, combined into composite steps, and/or separated into sub-stepsto provide features described herein.

It should be appreciated that like reference numerals are used toidentify like elements illustrated in one or more of the figures,wherein these labeled figures are for purposes of illustratingembodiments of the present disclosure and not for purposes of limitingthe same.

One aspect of the present disclosure is directed to a method fordetermining wafer inspection parameters. The method includes identifyingan area of interest in an IC design layout, performing an inspectionsimulation on the area of interest by generating a plurality ofsimulated optical images from the area of interest using a plurality ofoptical modes, and selecting, based on the simulated optical images, atleast one of the optical modes to use for inspecting an area of a waferthat is fabricated based on the area of interest in the IC designlayout.

In some embodiments, during the inspection simulation on the area ofinterest, each simulated optical image is generated using a respectiveoptical mode by performing the steps of: generating a first image fromthe area of interest using the optical mode, adding an artificial defectinto the area of interest, generating a second image from the area ofinterest with the artificial defect using the optical mode, andgenerating the simulated optical image as a difference image bysubtracting one of the first and second images from the other. In someembodiments, the artificial defect represents a DOI that is possible tooccur in the area of the wafer during fabrication. A type of the DOI anda location of the DOI are specified when the artificial defect is addedinto the area of interest. The selected optical mode is for use ininspecting the DOI in the area of interest. In some embodiments, thearea of interest surrounding the DOI has a size of about 200×200um{circumflex over ( )}2 or less. In some embodiments, the methodfurther includes adding data relevant to the DOI to a database. The datarelevant to the DOI includes the selected optical mode, the type of theDOI, the location of the DOI, and the location of the area of interest.In some embodiments, the plurality of optical modes include first andsecond optical modes. The plurality of simulated optical images includefirst and second simulated optical images that are generated from thearea of interest using the first and second optical modes, respectively.Selecting at least one of the optical modes based on the simulatedoptical images includes: computing one or more attributes of the firstsimulated optical image, computing the one or more attributes of thesecond simulated optical image, and evaluating the first and secondoptical modes by comparing the attributes of the first simulated opticalimage with corresponding attributes of the second simulated opticalimage. In some embodiments, the first and second optical modes eachinclude optical parameters selected from the group consisting of pixelsize, optical wavelength, aperture shape, optical polarization, andfocus setting. At least one of the optical parameters has differentparameter values for the first and second optical modes. In someembodiments, the one or more attributes include a signal to noise ratio.

Another aspect of the present disclosure is directed to an inspectionsystem including a computer system that is configured to select aportion of an IC design layout and perform an inspection simulation onthe portion of the IC design layout. The inspection simulation includesusing an optical mode to generate a simulated optical image from theportion of the IC design layout. The computer system is furtherconfigured to determine one or more attributes of the simulated opticalimage for evaluation of the optical mode. In some embodiments, theinspection system further includes an optical system configured tooptically inspect an area of a wafer using the optical mode. The area ofthe wafer is fabricated based at least in part on the portion of the ICdesign layout.

In some embodiments, the optical mode is a first optical mode and thesimulated optical image is a first simulated optical image. The computersystem is further configured to: use a second optical mode to generate asecond simulated optical image from the portion of the IC design layout,determine the one or more attributes of the second simulated opticalimage, and evaluate the first and second optical modes by comparing theattributes of the first simulated optical image with correspondingattributes of the second simulated optical image. In some embodiments,the computer system is further configured to designate one of the firstand second optical modes in a database as a preferred mode forinspecting the portion of the IC design layout. In some embodiments, thefirst and second optical modes each include optical parameters selectedfrom the group consisting of pixel size, optical wavelength, apertureshape, optical polarization, and focus setting. At least one of theoptical parameters has different parameter values for the first andsecond optical modes. In some embodiments, using the optical mode togenerate the simulated optical image from the portion of the IC designlayout includes: generating a first image from the portion of the ICdesign layout using the optical mode, adding an artificial defect intothe portion of the IC design layout, generating a second image from theportion of the IC design layout with the added artificial defect usingthe optical mode, and generating the simulated optical image as adifference image by subtracting one of the first and second images fromthe other. In some embodiments, the artificial defect represents a DOIthat is possible to occur within an area of a wafer that is fabricatedbased on the portion of the IC design layout. A type of the DOI and alocation of the DOI are specified when the artificial defect is addedinto the portion of the IC design layout. In some embodiments, theportion of the IC design layout is based on a 3D model, and wherein theinspection simulation further includes using FDTD equations to solveMaxwell boundary conditions. In some embodiments, evaluating the firstand second optical modes by comparing the first and second defectdetection signals includes using at least one of machine learning,random forest, and mixed model algorithms.

Yet another aspect of the present disclosure is directed to a method forretraining wafer inspection parameters. The method includes performingan optical inspection on an area of a wafer that has been fabricatedbased on an IC design layout, where the optical inspection includesusing a set of optical parameters to generate an optical image from thearea of the wafer. The method further includes generating one or moredefect detection signals from the optical image, and determining, basedon the one or more defect detection signals, whether to modify values ofthe set of optical parameters for inspecting the area of the wafer. Insome embodiments, the method further includes modifying the values ofone or more of the set of optical parameters based upon thedetermination that the one or more defect detection signals have failedto meet predefined criteria. In some embodiments, the set of opticalparameters is associated with a first defect according to a databasethat stores information relevant to the first defect. The area of thewafer includes a second defect that differs from the first defect. Themethod further includes, after modifying the values of one or more ofthe set of optical parameters, adding information relevant to the seconddefect into the database. The information relevant to the second defectincludes a type of the second defect, a location of the second defect,and the modified values of the set of optical parameters.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

What is claimed is:
 1. A method comprising: generating a first opticalimage of an area of an integrated circuit design layout having a defectby using a first set of optical parameters; generating a second opticalimage of the area of the integrated circuit design layout having thedefect by using a second set of optical parameters, the second set ofoptical parameters being different than the first set of opticalparameters; generating a first defect detection signal from the firstoptical image; generating a second defect detection signal from thesecond optical image; determining that the second detection signal isbetter at identifying the defect than the first detection signal; andafter determining that the second detection signal is better atidentifying the defect than the first detection signal, evaluating anarea on a wafer using the second set of optical parameters to determinewhether the area includes the defect.
 2. The method of claim 1, furthercomprising: selecting the area of the integrated circuit design layoutbased on empirical data indicating that the defect is likely to occur inthe area; and inserting the defect into the area of the integratedcircuit design layout.
 3. The method of claim 1, wherein the defect isselected from the group consisting of a cut metal failure, a damagedfin, a gate extrusion, a merged metal line and a missing via.
 4. Themethod of claim 1, further comprising modifying an optical parameterfrom the first set of optical parameters to form the second set ofoptical parameters, and wherein the optical parameter is selected fromthe group consisting of pixel size, optical wavelength, aperture shape,optical polarization and focus setting.
 5. The method of claim 1,wherein the determining that the second detection signal is better atidentifying the defect than the first detection signal is performed byan artificial intelligence structure.
 6. The method of claim 1, whereinthe first detection signal includes a first signal to noise (S/N) ratio,wherein the second detection signal includes a second S/N ratio, andwherein determining that the second detection signal is better atidentifying the defect than the first detection signal includescomparing the second S/N ratio to the first S/N ratio.
 7. The method ofclaim 1, further comprising: determining that the evaluating of the areaon the wafer using the second set of optical parameters fails to meet apredetermined criteria; modifying an optical parameter from the secondset of optical parameters to form a third set of optical parameters; andevaluating the area on the wafer using the third set of opticalparameters to determine whether the area includes the defect.
 8. Amethod comprising: generating a first optical image of an area of anintegrated circuit design layout by using a first set of opticalparameters; inserting a defect into the area of the integrated circuitdesign layout; generating a second optical image of the area of theintegrated circuit design layout having the defect by using the firstset of optical parameters; generating a first optical difference imageby comparing the first optical image to the second optical image;generating a first defect detection signal from the first opticaldifference image, wherein the defect is not identified by the firstdefect detection; generating a third optical image of the area of theintegrated circuit design layout having the defect by using a second setof optical parameters, the second set of optical parameters beingdifferent than the first set of optical parameters; generating a secondoptical difference image by comparing the first optical image to thethird optical image; generating a second defect detection signal fromthe second optical difference image, wherein the defect is identified bythe second defect detection signal; and evaluating an area on a waferusing the second set of optical parameters to determine whether the areaincludes the defect.
 9. The method of claim 8, wherein the second set ofoptical parameters differs from the first set of optical parameters byhaving a different value associated with at least one optical parameter,and wherein the at least one optical parameter is selected from thegroup consisting of pixel size, optical wavelength, aperture shape,optical polarization and focus setting.
 10. The method of claim 8,further comprising updating data relevant to the defect in a database,wherein the data relevant to the defect includes the second set ofoptical parameters, a type of defect, a location of the defect in theintegrated circuit design layout and a location of the area in theintegrated circuit design layout.
 11. The method of claim 8, wherein theinserting of the defect into the area of the integrated circuit designlayout occurs after the generating of the first optical image of thearea of the integrated circuit design layout by using the first set ofoptical parameters.
 12. The method of claim 8, further comprising:generating a fourth optical image of the area of the integrated circuitdesign layout having the defect by using a third set of opticalparameters, the third set of optical parameters being different than thefirst and second set of optical parameters; generating a third opticaldifference image by comparing the first optical image to the fourthoptical image; generating a third defect detection signal from the thirdoptical difference image, wherein the defect is identified by the thirddefect detection signal; and determining that the second set of opticalparameters is better at identifying the defect than the third set ofoptical parameters.
 13. The method of claim 12, wherein determining thatthe second set of optical parameters is better at identifying the defectthan the third set of optical parameters includes comparing the seconddefect detection signal and the third defect detection signal.
 14. Themethod of claim 8, wherein the defect is a hot spot defect.
 15. Themethod of claim 8, wherein the defect is a nuisance defect.
 16. A methodcomprising: generating a first optical image based on an area of anintegrated circuit design layout by using a first set of opticalparameters; generating a first defect detection signal from the firstoptical image, wherein a defect is not identified by the first defectdetection signal; identifying a second set of optical parameters that isdifferent than the first set of optical parameters; generating a secondoptical image based on the area of the integrated circuit design layoutby using the second set of optical parameters; generating a seconddefect detection signal from the second optical image, wherein thedefect is identified by the second defect detection signal; andinspecting an area on a wafer using the second set of opticalparameters, the wafer having been fabricated based on the integratedcircuit design layout and the area of the wafer corresponding to thearea of the integrated circuit design layout.
 17. The method of claim16, further comprising identifying the area of the integrated circuitdesign layer based on a probability of the area having the defect. 18.The method of claim 16, wherein the defect is selected from the groupconsisting of an unknown type of defect at a known location, a knowntype of defect at a new location, and a new type of defect at a newlocation.
 19. The method of claim 16, wherein identifying the second setof optical parameters that is different than the first set of opticalparameters includes: identifying the closest known defect to the defect;and identifying the second set of optical parameters that are associatedwith identifying the known defect.
 20. The method of claim 16, whereinthe second set of optical parameters differs from the first set ofoptical parameters by having a different value associated with at leastone optical parameter, and wherein the at least one optical parameter isselected from the group consisting of pixel size, optical wavelength,aperture shape, optical polarization and focus setting.